How to Track Your Brand in Chinese AI Search (DeepSeek, Kimi, Qwen, GLM, Doubao)

Why does Chinese AI search visibility matter now?

A growing share of high-intent buying research in Greater China, Singapore and the wider APAC region never touches ChatGPT or Google. Chinese users resolve "which vendor, which product, is this company legit" questions inside domestic assistants: DeepSeek, Alibaba's Qwen/Tongyi, Moonshot's Kimi, Zhipu's GLM, and ByteDance's Doubao. For a fintech, B2B or cross-border brand selling into that market, being invisible in those answers is the 2026 equivalent of not ranking on Google in 2010. Watch only Western engines and you are measuring the wrong half of your funnel.

Why do Western GEO tools miss Chinese engines?

Chinese engines run in a different technical and linguistic environment, and most vendors never built for it. The gap comes down to four hard problems stacked together. Access comes first: several Chinese assistants gate their APIs behind mainland accounts, phone verification, or region-locked apps, so a US-hosted crawler can't just query them. Then language. Visibility and sentiment scoring have to run on native Chinese output; run it on a machine translation and the read is wrong. The source graph is the third problem. These models retrieve and cite from Baidu, Zhihu, WeChat public accounts, Xiaohongshu and Chinese media, none of Reddit, Wikipedia or G2. Fourth, the prompts. The way a Chinese buyer phrases a comparison query is nowhere near a literal translation of the English one. A tool that skips any one of these hands you a number that looks fine and means nothing. Most Western dashboards were scoped to the English-language, US-source world, so the Chinese half stays dark.

What should you measure inside Chinese AI search?

The same four metrics you'd track in ChatGPT (visibility, share of voice, cited sources, and sentiment), computed per Chinese engine and in native Chinese. For each of DeepSeek, Kimi, Qwen/Tongyi, GLM/Zhipu and Doubao, you want to know:

• Presence / visibility, across your priority prompt set, how often does the engine mention your brand at all?
• Share of voice, when it names vendors, what fraction are you vs. named competitors?
• Cited sources, which pages and domains the answer pulls from, so you know what to influence (often a Zhihu thread or a WeChat article, not your own site).
• Sentiment & accuracy, is the description of your brand correct, and is the tone positive, neutral or negative?
• Prompt-level detail, which exact queries you win and lose, so fixes are targeted.

Keeping these identical to your Western metrics buys you comparability: one framework, two engine families, so you can see at a glance that you're strong in Perplexity and absent in Kimi, then act on it. The discipline mirrors how to monitor AI citations, extended to a second engine family.

How do you actually track it, step by step?

Build one prompt set, localize it, run it against every engine on a schedule, and diff the results over time. A workable process:

• 1. Define the buying prompts. List the real questions a Chinese-market buyer asks: "best [category] provider," "is [your brand] trustworthy," "[you] vs [competitor]." Write them the way a native speaker types them, not as translations.
• 2. Localize, don't translate. Adapt phrasing, product names and category terms to Chinese search behavior.
• 3. Run against all engines. Query DeepSeek, Kimi, Qwen/Tongyi, GLM/Zhipu and Doubao, plus your Western set, from an environment that can reach them.
• 4. Score in native language. Compute visibility, share of voice, sources and sentiment on the raw Chinese output.
• 5. Track over time. Re-run on a cadence so you see whether a content fix moved the needle, and get alerted when a competitor overtakes you.

You can do steps 1 through 5 by hand with a spreadsheet and a stack of accounts, but it doesn't scale and it drifts. A monitoring platform earns its keep on exactly the parts that break when hand-rolled: Chinese-engine access, native scoring and scheduling.

How do you improve your visibility once you've measured it?

Influence the sources these engines retrieve from, in the language they retrieve in. Because Chinese models cite a different source graph, generic English GEO work barely moves them. What tends to help: publish accurate, structured Chinese-language content about your brand and category; get represented on the platforms these engines lean on, meaning Zhihu answers, WeChat public-account articles, reputable Chinese media, Baidu Baike-style entity pages; keep your own site crawlable and factually clear so a model that does reach it gets the brand description right; and correct inaccuracies early, before a wrong "fact" repeated across sources compounds. The measurement loop points you at the specific source feeding a bad or missing answer, so you fix that source instead of shouting into the void. Treat it as generative engine optimization applied to a second market; if the concept is new, start with what is generative engine optimization.

Which tools can track Chinese AI engines?

Very few, and that is the whole point of this piece. The table below compares platforms buyers commonly evaluate. Every vendor listed is a legitimate GEO/AI-visibility tool. The differentiator here is narrow: whether the product is positioned to monitor Chinese engines at all.

*Not positioned for Chinese engines means the vendor's public product pages, at the time of writing, market Western-engine coverage and do not advertise DeepSeek/Kimi/Qwen/GLM/Doubao monitoring. It is not a claim that the capability is technically impossible for them.

Frequently asked questions

References

• QuestMobile via SCMP, Doubao and DeepSeek MAU rankings (Aug 2025)
• Sensor Tower / TechCrunch, DeepSeek tops the App Store (Jan 2025)
• Vendor sites: Geolix.ai, Profound, Peec AI, Writesonic, Quattr, Otterly.ai, AthenaHQ, Dageno, Scrunch AI (product capabilities drawn from each vendor's public site).